The software and hardware landscape of high performance computing is expanding faster than computational scientists can take advantage of new frameworks and platforms. In an ideal world, simulation codes would be written once in a high-level manner and achieve high-performance anywhere, but the reality is more complicated. Currently, high-level solutions lack support for sophisticated physical models across different parallel backends. Existing solutions with appropriate support are low-level and, therefore, tied to a specific hardware target. We present an approach that tackles this problem with a modularized separation of concerns: a middle layer separates the management of generating low-level optimized code from a high-level programmable layer. In this paper, we describe how our contributions to this hardware-agnostic, middle-layer language provide functionality for complex room acoustics simulations, a type of Finite Difference Time Domain (FDTD) simulation using stencils which is representative of many other 3D wave models. We show that we are able to develop performance-portable codes for these types of models which leads to performance on par with tuned hand-written implementations. Furthermore, we show how this approach is used to develop both host and device side code for multi-kernel applications, as is required for room acoustics simulations with complex boundaries.

Stencil computations are a widely used type of algorithm, found in applications from physical simulations to machine learning. Stencils are embarrassingly parallel, therefore fit on modern hardware such as Graphic Processing Units perfectly. Although stencil computations have been extensively studied, optimizing them for increasingly diverse hardware remains challenging. Domain-specific Languages (DSLs) have raised the programming abstraction and offer good performance; however, this method places the burden on DSL implementers to write almost full-fledged parallelizing compilers and optimizers. Lift has recently emerged as a promising approach to achieve performance portability by using a small set of reusable parallel primitives that DSL or library writers utilize. L ift ’s key novelty is in its encoding of optimizations as a system of extensible rewrite rules which are used to explore the optimization space. This article demonstrates how complex multi-dimensional stencil code and optimizations are expressed using compositions of simple 1D L ift primitives and rewrite rules. We introduce two optimizations that provide high performance for stencils in particular: classical overlapped tiling for multi-dimensional stencils and 2.5D tiling specifically for 3D stencils. We provide an in-depth analysis on how the tiling optimizations affects stencils of different shapes and sizes across different applications. Our experimental results show that our approach outperforms existing compiler approaches and hand-tuned codes.

Stencil computations are widely used from physical simulations to machine-learning. They are embarrassingly parallel and perfectly fit modern hardware such as Graphic Processing Units. Although stencil computations have been extensively studied, optimizing them for increasingly diverse hardware remains challenging. Domain Specific Languages (DSLs) have raised the programming abstraction and offer good performance. However, this places the burden on DSL implementers who have to write almost full-fledged parallelizing compilers and optimizers.